Scalable Cloud Migration for Power BI With the Data Factory
Performance Bottlenecks and Inefficient Data Processing in Reporting
Many companies are faced with the challenge that Power BI reports are becoming more structured and complex due to years of growth and are losing performance as a result.
A leading OEM from the DACH region faced precisely this challenge. The reasons are often manifold: manual data processing, inconsistent transformation processes and an infrastructure that reaches its limits with increasing data volumes. Particularly problematic are computationally intensive transformations within Power BI, which cause long loading times and lead to inefficient workflows.
Optimization of Data Processing -
With the Data Factory
Decoupling compute-intensive processes from Power BI
All data-intensive transformations must be removed from Power BI and carried out efficiently via an ETL process.- Automated data processing with high-performance scripts
Development of a scalable ETL architecture that enables structured data preparation. This ensures that the data is provided in an optimal format so that Power BI can access it efficiently and with high performance. - Use of a data factory for data integration
The implementation of a powerful data factory to centrally collect data from various sources, transform it and make it available in a structured format.
Building a Scalable Infrastructure
Flexibility through cloud-based processing
Data processing should no longer be linked to a local, hardware-bound environment, but should take place in a scalable cloud infrastructure that offers higher availability and computing power.
A Cloud-Based Etl Architecture With AWS Based on the Doubleslash Data Factory
Our solution was based on a holistic optimization of the existing system through the following measures:
Outsourcing the Data Logic
The complex transformations from Power BI were converted into high-performance, stable data processing. Optimized Python and PySpark scripts were used, which work efficiently in a cloud environment.
Automated ETL Processes
A scalable ETL pipeline was implemented to provide data in a structured form. This significantly reduced the load on Power BI.
Use of a Cloud Infrastructure With AWS
The existing local dependency was replaced by a scalable cloud solution using services such as S3 for storage, Fargate and ECS for container management, VPC for network integration, CloudWatch for logging and KMS for data encryption.
Development of a Release Process
A standardized release process was introduced to ensure long-term stability. This ensures that changes to data processing can be made seamlessly and in a controlled manner.
Scalable, High-Performance and Efficient Working With Power BI
The implementation of the doubleSlash Data Factory has resulted in decisive improvements:
- Massive increase in performance
As Power BI now only accesses optimally prepared data, the loading time of the dashboard has been significantly reduced. - Structured and clean database
The use of an AWS Data Factory ensures that unstructured raw data is reliably transformed into a cleansed format.
- Scalable and future-proof infrastructure
Thanks to the cloud-based architecture, increasing data volumes can be processed easily without local bottlenecks occurring. - Reduction of manual processes
The automation of data processing minimizes sources of error and ensures consistent data quality.
The optimized data processing and scalable infrastructure provide the OEM with a stable and high-performance reporting solution. This allows data-driven decisions to be made quickly and efficiently.
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